{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3V5XZVHek6xk"
   },
   "source": [
    " # Building Agentic RAG Pipelines for Financial Document Analysis with Contextual AI 🚀\n",
    " \n",
    " Building Retrieval-Augmented Generation (RAG) pipelines can seem daunting, with lots of moving parts and custom logic. In this tutorial, you'll learn how to quickly set up RAG agents using **Contextual AI’s managed platform**. You'll also get hands-on with several of the agent's core components—like the Parser, Reranker, Grounded Language Model, and LMUnit—so you can see how each part works in practice.\n",
    " \n",
    " ## 🎯 What You'll Build\n",
    " \n",
    " In this hands-on tutorial, you'll explore **core RAG techniques** with Contextual AI while creating an agent for **financial document analysis and quantitative reasoning** from scratch.\n",
    " \n",
    " \n",
    " <div align=\"center\">\n",
    " <img src=\"https://contextual.ai/wp-content/uploads/2025/08/contextual-architecture-1.png\" alt=\"Contextual Architecture\" width=\"1000\"/>\n",
    " </div>\n",
    " \n",
    " ### Learning Outcomes\n",
    " By completing this tutorial, you'll learn how to **leverage agentic RAG to solve more complex queries**. The agentic nature lies in the system's ability to autonomously analyze incoming queries, determine what reformulation strategy is needed, and execute that strategy without explicit user instruction.\n",
    " \n",
    " Traditional RAG systems take queries as-is, often leading to poor retrievals for ambiguous, context-lacking, or complex queries. Agentic RAG intelligently preprocesses queries to bridge this gap. In the query path, the primary agentic step is query reformulation, comprising multi-turn, query expansion, or query decomposition. This query reformulation step is critical to obtaining the most robust RAG results, and is one component of a system engineered to generate the most accurate query responses.\n",
    " \n",
    " In query reformulation, context is added or queries are restructured from the original input: for multi-turn, adding iterative dialogue context; for query expansion, adding additional context to help a short query return optimal results; for query decomposition, taking complex multi-faceted queries that require reasoning across several unrelated documents, and breaking them down into several sub-queries that help obtain the most relevant retrievals. This agentic component handles all of this reformulation autonomously, augmenting the user's query to help obtain the response they need.\n",
    " <div align=\"center\">\n",
    " <img src=\"https://contextual.ai/wp-content/uploads/2025/09/retrieval_pipeline.png\" alt=\"Contextual Architecture\" width=\"1000\"/>\n",
    " </div>\n",
    " \n",
    " \n",
    " You will set up your Agentic RAG system by learning to:\n",
    " - **Configure document datastores** with indexing tuned for RAG performance  \n",
    " - **Deploy production-ready agents** with robust instructions and safeguards  \n",
    " - **Query the system interactively** using natural language while maintaining strict grounding  \n",
    " - **Continuously validate and improve** your pipeline with automated testing and performance metrics  \n",
    " \n",
    " You’ll also gain practical experience with **four fundamental RAG components in Contextual AI**:\n",
    " 1. **Parser** – Ingest and structure heterogeneous documents (reports, tables, figures) for retrieval.  \n",
    " 2. **Reranker** – Dynamically select the most relevant evidence to ensure precise grounding.  \n",
    " 3. **Grounded Language Model (GLM)** – Generate factual, source-backed responses using the retrieved context.  \n",
    " 4. **Language Model Unit Tests (LMUnits)** – Automatically evaluate and optimize the accuracy, grounding, and reliability of your agent.  \n",
    " \n",
    " ⏱️ This tutorial runs end-to-end in under **15 minutes**. Every step can also be done via GUI for a **no-code RAG workflow**.\n",
    " \n",
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "clV0jqJl0K96"
   },
   "source": [
    "# Building a RAG Agent from Scratch\n",
    "\n",
    "Before diving into individual RAG techniques, let’s **build a complete RAG agent end-to-end** from scratch.  \n",
    "\n",
    "## 🛠️ Environment Setup\n",
    "\n",
    "First, we'll install the required dependencies and set up our development environment. The `contextual-client` library provides Python bindings for the Contextual AI platform, while the additional packages support data visualization and progress tracking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XkSHhUakic9Y",
    "outputId": "8afe50b1-8546-4c74-cdfb-f44b74c8af13"
   },
   "outputs": [],
   "source": [
    "# Install required packages for Contextual AI integration and data visualization\n",
    "%pip install contextual-client matplotlib tqdm requests pandas dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6f6q2NaCN66d"
   },
   "source": [
    "Next, we'll import the necessary libraries that we'll use throughout this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GJh1fBBWic9Y"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "from pathlib import Path\n",
    "from typing import List, Optional, Dict\n",
    "from IPython.display import display, JSON\n",
    "import pandas as pd\n",
    "from contextual import ContextualAI\n",
    "import ast\n",
    "from IPython.display import display, Markdown\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Db9S8YTKijVt"
   },
   "source": [
    "---\n",
    "\n",
    "## 🔑 Step 1: API Authentication Setup\n",
    "\n",
    "### Getting Your Contextual AI API Key\n",
    "\n",
    "Before we can start building our RAG agent, you'll need access to the Contextual AI platform.\n",
    "\n",
    "If you do not have an account yet, you can create a workspace with a **30-day free trial** of an agent and datastore.\n",
    "\n",
    "### Step-by-Step API Key Setup:\n",
    "\n",
    "1. **Create Your Account**: Visit [app.contextual.ai](https://app.contextual.ai?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook) and click the **\"Start Free\"** button\n",
    "2. **Navigate to API Keys**: Once logged in, find **\"API Keys\"** in the sidebar\n",
    "3. **Generate New Key**: Click **\"Create API Key\"** and follow the setup steps\n",
    "4. **Store Securely**: Copy your API key and store it safely (you won't be able to see it again)\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://contextual.ai/wp-content/uploads/2025/08/API.png\" alt=\"API\" width=\"800\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8s-th42PN66e"
   },
   "source": [
    "### Configuring Your API Key\n",
    "\n",
    "To run this tutorial, you can store your API key in a `.env` file. This keeps your keys separate from your code. After setting up your .env file, you can load the API key from `.env` to initialize the Contextual AI client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "CUbmJvzoNzIV"
   },
   "outputs": [],
   "source": [
    "# Load API key from .env\n",
    "from dotenv import load_dotenv\n",
    "import os\n",
    "load_dotenv()\n",
    "\n",
    "# Initialize with your API key\n",
    "API_KEY = os.getenv(\"CONTEXTUAL_API_KEY\")\n",
    "client = ContextualAI(\n",
    "    api_key=API_KEY\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lcqh_-j1MzCn"
   },
   "source": [
    "---\n",
    "\n",
    "## 📊 Step 2: Create Your Document Datastore\n",
    "\n",
    "### Understanding Datastores\n",
    "\n",
    "A **datastore** in Contextual AI is a secure, isolated container for your documents and their processed representations. Each datastore provides:\n",
    "\n",
    "- **Isolated Storage**: Documents are kept separate and secure for each use case\n",
    "- **Intelligent Processing**: Automatic parsing, chunking, and indexing of uploaded documents\n",
    "- **Optimized Retrieval**: High-performance search and ranking capabilities\n",
    "\n",
    "Let's create a datastore for our financial document analysis agent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wlulbIvjdbZA",
    "outputId": "350eda1d-b1c6-408e-dc80-262c144fa311"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created new datastore with ID: 1335c317-61a1-4aad-8a97-ac6a6cdedc8b\n"
     ]
    }
   ],
   "source": [
    "datastore_name = 'Financial_Demo_RAG'\n",
    "\n",
    "# Check if datastore exists\n",
    "datastores = client.datastores.list()\n",
    "existing_datastore = next((ds for ds in datastores if ds.name == datastore_name), None)\n",
    "\n",
    "if existing_datastore:\n",
    "    datastore_id = existing_datastore.id\n",
    "    print(f\"Using existing datastore with ID: {datastore_id}\")\n",
    "else:\n",
    "    result = client.datastores.create(name=datastore_name)\n",
    "    datastore_id = result.id\n",
    "    print(f\"Created new datastore with ID: {datastore_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_IkAep8Vf29_"
   },
   "source": [
    "---\n",
    "\n",
    "## 📄 Step 3: Document Ingestion and Processing\n",
    "\n",
    "Now that your agent's datastore is set up, let's add some financial documents to it. Contextual AI's document processing engine provides **enterprise-grade parsing** that expertly handles:\n",
    "\n",
    "- **Complex Tables**: Financial data, spreadsheets, and structured information\n",
    "- **Charts and Graphs**: Visual data extraction and interpretation\n",
    "- **Multi-page Documents**: Long reports with hierarchical structure\n",
    "\n",
    "For this tutorial, we'll use sample financial documents that demonstrate various challenging scenarios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "3AbgklrUCEYB"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "\n",
    "# Create data directory if it doesn't exist\n",
    "if not os.path.exists('data'):\n",
    "    os.makedirs('data')\n",
    "\n",
    "# File list with corresponding GitHub URLs\n",
    "files_to_upload = [\n",
    "    # NVIDIA quarterly revnue 24/25\n",
    "    (\"A_Rev_by_Mkt_Qtrly_Trend_Q425.pdf\", \"https://raw.githubusercontent.com/ContextualAI/examples/refs/heads/main/08-ai-workshop/data/A_Rev_by_Mkt_Qtrly_Trend_Q425.pdf\"),\n",
    "    # NVIDIA quarterly revenue 22/23\n",
    "    (\"B_Q423-Qtrly-Revenue-by-Market-slide.pdf\", \"https://raw.githubusercontent.com/ContextualAI/examples/refs/heads/main/08-ai-workshop/data/B_Q423-Qtrly-Revenue-by-Market-slide.pdf\"),\n",
    "    # Spurious correlations report - fun example of graphs and statistical analysis\n",
    "    (\"C_Neptune.pdf\", \"https://raw.githubusercontent.com/ContextualAI/examples/refs/heads/main/08-ai-workshop/data/C_Neptune.pdf\"),\n",
    "    # Another spurious correlations report - fun example of graphs and statistical analysis\n",
    "    (\"D_Unilever.pdf\", \"https://raw.githubusercontent.com/ContextualAI/examples/refs/heads/main/08-ai-workshop/data/D_Unilever.pdf\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xLYsG5B0P1Dk"
   },
   "source": [
    "### Document Download and Ingestion Process\n",
    "The following cell downloads example documents locally from the GitHub links above, uploads them to Contextual AI, and tracks their processing status and IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ct8LHuvgPkyR",
    "outputId": "042cb0b1-1a23-4d5b-8dad-2fb0742039a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data/A_Rev_by_Mkt_Qtrly_Trend_Q425.pdf\n",
      "Successfully uploaded A_Rev_by_Mkt_Qtrly_Trend_Q425.pdf to datastore 1335c317-61a1-4aad-8a97-ac6a6cdedc8b\n",
      "Fetching data/B_Q423-Qtrly-Revenue-by-Market-slide.pdf\n",
      "Successfully uploaded B_Q423-Qtrly-Revenue-by-Market-slide.pdf to datastore 1335c317-61a1-4aad-8a97-ac6a6cdedc8b\n",
      "Fetching data/C_Neptune.pdf\n",
      "Successfully uploaded C_Neptune.pdf to datastore 1335c317-61a1-4aad-8a97-ac6a6cdedc8b\n",
      "Fetching data/D_Unilever.pdf\n",
      "Successfully uploaded D_Unilever.pdf to datastore 1335c317-61a1-4aad-8a97-ac6a6cdedc8b\n",
      "Successfully uploaded 4 files to datastore\n",
      "Document IDs: ['c75ff84c-0a01-49d4-91e4-2692d2627f39', 'e8c132e7-297f-4dd3-9682-d6bdacc64912', 'd43223be-ade5-4802-9ecc-3463e38853c1', '5002f4aa-d5ca-4e9b-b005-4ac156ac4bf0']\n"
     ]
    }
   ],
   "source": [
    "# Download and ingest all files\n",
    "document_ids = []\n",
    "for filename, url in files_to_upload:\n",
    "    file_path = f'data/{filename}'\n",
    "\n",
    "    # Download file if it doesn't exist\n",
    "    if not os.path.exists(file_path):\n",
    "        print(f\"Fetching {file_path}\")\n",
    "        try:\n",
    "            response = requests.get(url)\n",
    "            response.raise_for_status()  # Raise an exception for bad status codes\n",
    "            with open(file_path, 'wb') as f:\n",
    "                f.write(response.content)\n",
    "        except Exception as e:\n",
    "            print(f\"Error downloading {filename}: {str(e)}\")\n",
    "            continue\n",
    "\n",
    "    # Upload to datastore\n",
    "    try:\n",
    "        with open(file_path, 'rb') as f:\n",
    "            ingestion_result = client.datastores.documents.ingest(datastore_id, file=f)\n",
    "            document_id = ingestion_result.id\n",
    "            document_ids.append(document_id)\n",
    "            print(f\"Successfully uploaded {filename} to datastore {datastore_id}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error uploading {filename}: {str(e)}\")\n",
    "\n",
    "print(f\"Successfully uploaded {len(document_ids)} files to datastore\")\n",
    "print(f\"Document IDs: {document_ids}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vKOhbEqON66g"
   },
   "source": [
    "### Optional: Inspect Documents\n",
    "\n",
    "If you'd like to take a look at the ingested documents, you can do so via GUI at [https://app.contextual.ai](https://app.contextual.ai?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)\n",
    "\n",
    "1. Navigate to your workspace  \n",
    "2. Select **Datastores** on the left menu  \n",
    "3. Select **Documents**  \n",
    "4. Click on **Inspect** (once documents load)\n",
    "\n",
    "You will see something like this:\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://contextual.ai/wp-content/uploads/2025/08/datastore.png\" alt=\"Datastore\" width=\"800\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YXCAGHuwN66g"
   },
   "source": [
    "Once ingested, you can view the list of documents, see their metadata, and also delete documents via API.\n",
    "\n",
    "**Note:** It may take a few minutes for the document to be ingested and processed. If the documents are still being ingested, you will see `status='processing'`. Once ingestion is complete, the status will show as `status='completed'`.\n",
    "\n",
    "You can learn more about the metadata [here](https://docs.contextual.ai/api-reference/datastores-documents/get-document-metadata?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QW9saxkGN66g",
    "outputId": "80d8ebca-ec77-4b1d-c0d7-68dbd6029ce6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document metadata: DocumentMetadata(id='c75ff84c-0a01-49d4-91e4-2692d2627f39', created_at='2025-09-02T16:12:09.990120', name='A_Rev_by_Mkt_Qtrly_Trend_Q425.pdf', status='completed', custom_metadata={}, custom_metadata_config={}, has_access=True, ingestion_config={'parsing': {'figure_captioning_prompt': None, 'figure_caption_mode': 'default', 'enable_split_tables': True, 'max_split_table_cells': 100, 'ocr_level': 'auto', 'use_hyperlink_extraction': False, 'enable_vlm_hierarchy_inference': True, 'layout_model': 'dit', 'vlm_captioning_model': None, 'vlm_hierarchy_model': None, 'vlm_doc_name_model': None, 'vlm_markdown_reviser_model': None}, 'chunking': {'chunking_mode': 'hierarchy_depth', 'max_chunk_length_tokens': 768, 'min_chunk_length_tokens': 384, 'enable_hierarchy_based_contextualization': True, 'enable_contextualization': None, 'enable_section_based_contextualization': None}, 'html_config': {'max_recursive_depth': 5, 'markdown_links_mode': 'EXTERNAL', 'precise_image_attribution': True, 'enable_section_extraction': True, 'enable_table_links_addition': True, 'max_chunk_length_tokens': 768, 'enable_v2_extraction_pipeline': True}, 'ingestion_types': None, 'enable_v2_extraction_pipeline': True, 'extraction': None}, updated_at=None)\n"
     ]
    }
   ],
   "source": [
    "metadata = client.datastores.documents.metadata(datastore_id = datastore_id, document_id = document_ids[0])\n",
    "print(\"Document metadata:\", metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9a_xaq-K4TuP"
   },
   "source": [
    "---\n",
    "\n",
    "## 🤖 Step 4: Agent Creation and Configuration\n",
    "\n",
    "Now you'll create our RAG agent that will interact with the documents you just ingested.\n",
    "\n",
    "You can customize the Agent using additional parameters such as:\n",
    "\n",
    "- **`system_prompt`** is used for the instructions that your RAG system references when generating responses. Note that this is the default prompt as of 9.02.25.\n",
    "- **`suggested_queries`** is a user experience feature, to prepopulate queries for the agent so a new user can see interesting examples.  \n",
    "\n",
    "💡 Pro Tip: You can also configure or edit your agent in the UI at [app.contextual.ai](https://app.contextual.ai?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook), try changing the generation model to another LLM!  \n",
    "\n",
    "You can find all the additional parameters [here](https://docs.contextual.ai/api-reference/agents/create-agent?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DvU_PEFnJhDr",
    "outputId": "53e42cc7-126a-45d6-b707-2e610242a5bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating new agent\n",
      "Agent ID created: 296dc525-3bfc-4ed8-8513-658b9219b93d\n"
     ]
    }
   ],
   "source": [
    "system_prompt = '''\n",
    "You are a helpful AI assistant created by Contextual AI to answer questions about relevant documentation provided to you. Your responses should be precise, accurate, and sourced exclusively from the provided information. Please follow these guidelines:\n",
    "* Only use information from the provided documentation. Avoid opinions, speculation, or assumptions.\n",
    "* Use the exact terminology and descriptions found in the provided content.\n",
    "* Keep answers concise and relevant to the user's question.\n",
    "* Use acronyms and abbreviations exactly as they appear in the documentation or query.\n",
    "* Apply markdown if your response includes lists, tables, or code.\n",
    "* Directly answer the question, then STOP. Avoid additional explanations unless specifically relevant.\n",
    "* If the information is irrelevant, simply respond that you don't have relevant documentation and do not provide additional comments or suggestions. Ignore anything that cannot be used to directly answer this query.\n",
    "'''\n",
    "\n",
    "agent_name = \"Demo\"\n",
    "\n",
    "# Get list of existing agents\n",
    "agents = client.agents.list()\n",
    "\n",
    "# Check if agent already exists\n",
    "existing_agent = next((agent for agent in agents if agent.name == agent_name), None)\n",
    "\n",
    "if existing_agent:\n",
    "    agent_id = existing_agent.id\n",
    "    print(f\"Using existing agent with ID: {agent_id}\")\n",
    "else:\n",
    "    print(\"Creating new agent\")\n",
    "    app_response = client.agents.create(\n",
    "        name=agent_name,\n",
    "        description=\"Helpful Grounded AI Assistant\",\n",
    "        datastore_ids=[datastore_id],\n",
    "        agent_configs={\n",
    "        \"global_config\": {\n",
    "            \"enable_multi_turn\": False # Turning this off for deterministic responses for this demo\n",
    "        }\n",
    "        },\n",
    "        suggested_queries=[\n",
    "            \"What was NVIDIA's annual revenue by fiscal year 2022 to 2025?\",\n",
    "            \"When did NVIDIA's data center revenue overtake gaming revenue?\",\n",
    "            \"What's the correlation between the distance between Neptune and the Sun and Burglary rates in the US?\",\n",
    "            \"What's the correlation between Global revenue generated by Unilever Group and Google searches for 'lost my wallet'?\",\n",
    "            \"Does this imply that Unilever Group's revenue is derived from lost wallets?\",\n",
    "            \"What's the correlation between the distance between Neptune and the Sun and Global revenue generated by Unilever Group?\"\n",
    "        ]\n",
    "    )\n",
    "    agent_id = app_response.id\n",
    "    print(f\"Agent ID created: {agent_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MkdUUIrQN66g"
   },
   "source": [
    "### Optional: Let's look at our Agent in the Platform\n",
    "Your agent is now available via GUI as well, if you'd like to try querying it there.\n",
    "\n",
    "Visit: [https://app.contextual.ai](https://app.contextual.ai?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)\n",
    "\n",
    "1. Navigate to your workspace  \n",
    "2. Select **Agents** from the left menu  \n",
    "3. Select your Agent  \n",
    "4. Try a suggested query or type your question  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5F3p2pU6Wfkz"
   },
   "source": [
    "---\n",
    "\n",
    "## 💬 Step 5: Query the Agent\n",
    "\n",
    "### Testing Your RAG Agent\n",
    "\n",
    "Now that our agent is configured and connected to our financial documents, let's test its capabilities with various types of queries.\n",
    "\n",
    "The required fields are:\n",
    "\n",
    "- **`agent_id`**: The unique identifier of your Agent  \n",
    "- **`messages`**: A list of message(s) forming the user query  \n",
    "\n",
    "Optional information includes parameters for `stream` and `conversation_id`. You can refer [here](https://docs.contextual.ai/api-reference/agents-query/query?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook) for more information.\n",
    "\n",
    "Let's try this query: **\"What was NVIDIA's annual revenue by fiscal year 2022 to 2025?\"**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BWnjE43cN66h",
    "outputId": "624086d0-4762-4d82-f5ce-71a68534cc67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the provided documentation, I can present NVIDIA's annual revenue for fiscal years 2022 through 2025. Here are the total annual figures derived from the quarterly data:\n",
      "\n",
      "For Fiscal Year 2022, the total annual revenue was $26,614 million, calculated from quarterly figures of $7,643 million in Q4, $7,103 million in Q3, $6,507 million in Q2, and $5,661 million in Q1.[2]()()\n",
      "\n",
      "For Fiscal Year 2023, the total annual revenue was $24,974 million, comprising quarterly revenues of $6,051 million in Q4, $5,931 million in Q3, $6,704 million in Q2, and $8,288 million in Q1.[2]()()\n",
      "\n",
      "For Fiscal Year 2024, the total annual revenue was $65,922 million, derived from quarterly figures of $22,103 million in Q4, $18,120 million in Q3, $13,507 million in Q2, and $7,192 million in Q1.[1]()()\n",
      "\n",
      "For Fiscal Year 2025, the total annual revenue was $130,497 million, calculated from quarterly revenues of $39,331 million in Q4, $35,082 million in Q3, $30,040 million in Q2, and $26,044 million in Q1.[1]()()\n",
      "\n",
      "These figures demonstrate a clear upward trend in NVIDIA's annual revenue across the specified fiscal years.\n"
     ]
    }
   ],
   "source": [
    "query_result = client.agents.query.create(\n",
    "    agent_id=agent_id,\n",
    "    messages=[{\n",
    "        \"content\": \"What was NVIDIA's annual revenue by fiscal year 2022 to 2025?\",\n",
    "        \"role\": \"user\"\n",
    "    }]\n",
    ")\n",
    "print(query_result.message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pEetPdUcN66h"
   },
   "source": [
    "There is lots more information you can access from the query result. You can display the retrieved documents, for example.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "pZxp5LMmA4Zr",
    "outputId": "075c8a83-6389-4ef0-f630-41d99e5f33f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Processing Document 1 ---\n",
      "Retrieval Info for Document 1:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Processing Document 2 ---\n",
      "Retrieval Info for Document 2:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total documents processed: 2\n"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "import io\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def display_base64_image(base64_string, title=\"Document\"):\n",
    "    # Decode base64 string\n",
    "    img_data = base64.b64decode(base64_string)\n",
    "\n",
    "    # Create PIL Image object\n",
    "    img = Image.open(io.BytesIO(img_data))\n",
    "\n",
    "    # Display using matplotlib\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "\n",
    "    return img\n",
    "\n",
    "# Retrieve and display all referenced documents\n",
    "for i, retrieval_content in enumerate(query_result.retrieval_contents):\n",
    "    print(f\"\\n--- Processing Document {i+1} ---\")\n",
    "\n",
    "    # Get retrieval info for this document\n",
    "    ret_result = client.agents.query.retrieval_info(\n",
    "        message_id=query_result.message_id,\n",
    "        agent_id=agent_id,\n",
    "        content_ids=[retrieval_content.content_id]\n",
    "    )\n",
    "\n",
    "    print(f\"Retrieval Info for Document {i+1}:\")\n",
    "\n",
    "    # Display the document image\n",
    "    if ret_result.content_metadatas and ret_result.content_metadatas[0].page_img:\n",
    "        base64_string = ret_result.content_metadatas[0].page_img\n",
    "        img = display_base64_image(base64_string, f\"Document {i+1}\")\n",
    "    else:\n",
    "        print(f\"No image available for Document {i+1}\")\n",
    "\n",
    "print(f\"\\nTotal documents processed: {len(query_result.retrieval_contents)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B_xGrpPmA4Zr"
   },
   "source": [
    "# RAG Components Deep Dive\n",
    "\n",
    "With a complete RAG agent in place, we can now **zoom in on the core techniques** that make it work. Let’s explore  **four key components** of a production-grade RAG system:\n",
    "\n",
    "1. Document Parser\n",
    "2. Instruction-Following Reranker\n",
    "3. Grounded Language Model (GLM)\n",
    "4. LMUnit: Natural Language Unit Testing\n",
    "\n",
    "Note that one key component is not listed here - that is the Datastore. We leverage an ElasticSearch vector database in our production ready RAG system, and have only included the components built by Contextual AI above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "woHzAHMhA4Zr"
   },
   "source": [
    "## 1. Document Parser\n",
    "\n",
    "Parsing complex, unstructured documents is the critical foundation for agentic RAG systems. Failures in parsing cause these systems to miss critical context, degrading response accuracy.\n",
    "\n",
    "Our document parser combines the best of custom vision, OCR, and vision language models, along with specialized tools like table extractors—achieving superior accuracy and reliability by excelling in the following areas:\n",
    "\n",
    "- **Document-level understanding vs. page-by-page parsing**: Our parser understands the section hierarchies of long documents, equipping AI agents to understand relationships across hundreds of pages to generate contextually supported, accurate answers.\n",
    "- **Minimized hallucinations**: Our multi-stage pipeline minimizes severe hallucinations while providing accurate bounding boxes and confidence levels for table extraction to audit its output.\n",
    "- **Superior handling of complex modalities**: Our advanced system orchestrates the best models and specialized tools to handle the most challenging document elements, such as tables, charts, and figures.\n",
    "\n",
    "\n",
    "### Document Hierarchy\n",
    "\n",
    "Unlike traditional parsers, Contextual AI's solution understands how each page fits within the document's holistic structure and hierarchy, enabling AI agents to navigate long, complex documents with the same understanding a human would have. We automatically infer a document's hierarchy and structure, which enables developers to add metadata to each chunk that describes its position in the document. This improves retrieval and allows agents to understand how different sections relate to each other to provide answers that connect information across hundreds of pages.\n",
    "\n",
    "For more information about Contextual AI's document parser, you can read this [blog](https://contextual.ai/blog/document-parser-for-rag/?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oqyM6uwxA4Zs"
   },
   "source": [
    "Now, let's use ContextualAI's parser to parse the landmark \"Attention is All You Need\" paper to demonstrate the parser's capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VONCRNHuA4Zs",
    "outputId": "6a1b77c5-6098-4f9a-f188-90e7bc3f9464"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded paper to data/attention-is-all-you-need.pdf\n"
     ]
    }
   ],
   "source": [
    "# Download the Attention is All You Need paper from arXiv\n",
    "url = \"https://arxiv.org/pdf/1706.03762\"\n",
    "file_path = \"data/attention-is-all-you-need.pdf\"\n",
    "\n",
    "with open(file_path, \"wb\") as f:\n",
    "    f.write(requests.get(url).content)\n",
    "\n",
    "print(f\"Downloaded paper to {file_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dXogAR0iA4Zs"
   },
   "source": [
    "We'll configure the parser with the following settings:\n",
    "- **parse_mode**: \"standard\" for complex documents that require VLMs and OCR\n",
    "- **figure_caption_mode**: \"concise\" for brief figure descriptions\n",
    "- **enable_document_hierarchy**: True to capture document structure\n",
    "- **page_range**: \"0-5\" to parse the first 6 pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0ykxgOBrA4Zs",
    "outputId": "30e556db-2442-4da5-f10e-6573b6c4be89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse job submitted with ID: c447079c-408e-4d57-927b-c935b49e04d2\n"
     ]
    }
   ],
   "source": [
    "# Setup headers for direct API calls\n",
    "base_url = \"https://api.contextual.ai/v1\"\n",
    "headers = {\n",
    "    \"accept\": \"application/json\",\n",
    "    \"authorization\": f\"Bearer {API_KEY}\"\n",
    "}\n",
    "\n",
    "# Submit parse job\n",
    "url = f\"{base_url}/parse\"\n",
    "\n",
    "config = {\n",
    "    \"parse_mode\": \"standard\",\n",
    "    \"figure_caption_mode\": \"concise\",\n",
    "    \"enable_document_hierarchy\": True,\n",
    "    \"page_range\": \"0-5\",\n",
    "}\n",
    "\n",
    "with open(file_path, \"rb\") as fp:\n",
    "    file = {\"raw_file\": fp}\n",
    "    result = requests.post(url, headers=headers, data=config, files=file)\n",
    "    response = json.loads(result.text)\n",
    "\n",
    "job_id = response['job_id']\n",
    "print(f\"Parse job submitted with ID: {job_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gtl7As1LA4Zs"
   },
   "source": [
    "\n",
    "Now let's retrieve the parsed results. The parser provides multiple output types:\n",
    "- **Markdown-document**: A single Markdown for the entire document\n",
    "- **Markdown-per-page**: A list of Markdowns for each page of the document\n",
    "- **Blocks-per-page**: Structured JSON representations of content blocks sorted by reading order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Cavk8TDAA4Zs",
    "outputId": "37e2cd81-82b4-476d-d7b4-2a13aedd01db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse job is completed.\n"
     ]
    }
   ],
   "source": [
    "# Get the parse results\n",
    "url = f\"{base_url}/parse/jobs/{job_id}/results\"\n",
    "\n",
    "output_types = [\"markdown-per-page\"]\n",
    "\n",
    "result = requests.get(\n",
    "    url,\n",
    "    headers=headers,\n",
    "    params={\"output_types\": \",\".join(output_types)},\n",
    ")\n",
    "\n",
    "result = json.loads(result.text)\n",
    "print(f\"Parse job is {result['status']}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9G2KX2flA4Zs"
   },
   "source": [
    "When the parse job is completed (e.g., the above status is \"Parse job is completed. \"), we can  examine the parsed content from the first page of the paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 719
    },
    "id": "p5I3g2iSA4Zs",
    "outputId": "97a46c17-3b0a-40f9-e700-b770990e7202"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works.\n",
       "\n",
       "# Attention Is All You Need\n",
       "\n",
       "Noam Shazeer ∗ Google Brain noam@google.com\n",
       "\n",
       "Ashish Vaswani ∗ Google Brain avaswani@google.com\n",
       "\n",
       "Niki Parmar ∗ Google Research nikip@google.com\n",
       "\n",
       "Jakob Uszkoreit ∗ Google Research usz@google.com\n",
       "\n",
       "Aidan N. Gomez ∗† University of Toronto aidan@cs.toronto.edu\n",
       "\n",
       "Llion Jones ∗ Google Research llion@google.com\n",
       "\n",
       "Łukasz Kaiser ∗ Google Brain lukaszkaiser@google.com\n",
       "\n",
       "Illia Polosukhin ∗‡ illia.polosukhin@gmail.com\n",
       "\n",
       "## Abstract\n",
       "\n",
       "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\n",
       "\n",
       "∗ Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\n",
       "\n",
       "† Work performed while at Google Brain.\n",
       "\n",
       "‡ Work performed while at Google Research."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the first page's parsed markdown\n",
    "if 'pages' in result and len(result['pages']) > 0:\n",
    "    display(Markdown(result['pages'][0]['markdown']))\n",
    "else:\n",
    "    print(\"No parsed content available. Please check if the job completed successfully.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jX5ISR8n1EWk"
   },
   "source": [
    "To see job results in an interactive manner and submit new jobs, navigate to the UI using the following link by running the cell below. Note you'll need to change `\"your-tenant-name\"` to your tenant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2TQmC9w183C",
    "outputId": "b671ffa0-d323-4a27-ef6e-f381a39af70a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://app.contextual.ai/your-tenant-name/components/parse?job=c447079c-408e-4d57-927b-c935b49e04d2\n"
     ]
    }
   ],
   "source": [
    "tenant = \"your-tenant-name\"\n",
    "print(f\"https://app.contextual.ai/{tenant}/components/parse?job={job_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pMjEeRd32K6Y"
   },
   "source": [
    "<div align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/ContextualAI/examples/6cb206bdaaf158fcdf2b01c102291c64381cba7a/03-standalone-api/04-parse/parse-ui.png\" alt=\"Document Hierarchy\" width=\"1000\"/>\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zG6za87T2hnH"
   },
   "source": [
    "For more example code for Contextual AI's Parser, see our [parse examples notebook](https://github.com/ContextualAI/examples/tree/main/03-standalone-api/04-parse?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2PxkRUhdA4Zt"
   },
   "source": [
    "## 2. Instruction-Following Reranker\n",
    "\n",
    "Enterprise RAG systems often deal with conflicting information in their knowledge bases. Marketing materials can conflict with product materials, documents in Google Drive could conflict with those in Microsoft Office, Q2 notes conflict with Q1 notes, and so on. You can tell our reranker how to resolve these conflicts with instructions like:\n",
    "\n",
    "- \"Prioritize internal sales documents over market analysis reports. More recent documents should be weighted higher. Enterprise portal content supersedes distributor communications.\"\n",
    "- \"Emphasize forecasts from top-tier investment banks. Recent analysis should take precedence. Disregard aggregator sites and favor detailed research notes over news summaries.\"\n",
    "\n",
    "This enables an unprecedented level of control that improves RAG performance significantly.\n",
    "\n",
    "\n",
    "### State-of-the-Art Performance\n",
    "\n",
    "Contextual AI's SOTA reranker (v2) is the most accurate in the world with or without instructions – outperforming competitors by large margins on the industry-standard BEIR benchmark (V1), our internal financial and field engineering datasets (V1), and our novel instruction-following reranker evaluation datasets (V1).\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://contextual.ai/wp-content/uploads/2025/08/Reranker-V2-slide-1.png\" alt=\"Document Hierarchy\" width=\"1000\"/>\n",
    "</div>\n",
    "\n",
    "\n",
    "For more information about Contextual AI's reranker V2, you can read this [blog](https://contextual.ai/blog/rerank-v2/?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook), where we also share links to open-source weights and our novel evaluation dataset.\n",
    "\n",
    "For more information about Contextual AI's reranker V1, you can read this [blog](https://contextual.ai/blog/introducing-instruction-following-reranker/?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2etJISDvA4Zt"
   },
   "source": [
    "Let's demonstrate the reranker's instruction-following capabilities with a realistic enterprise scenario. We'll use a query about enterprise GPU pricing and see how the reranker handles conflicting information based on our instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "rP00ajxwA4Zt"
   },
   "outputs": [],
   "source": [
    "# Define our query and instruction\n",
    "query = \"What is the current enterprise pricing for the RTX 5090 GPU for bulk orders?\"\n",
    "\n",
    "instruction = \"Prioritize internal sales documents over market analysis reports. More recent documents should be weighted higher. Enterprise portal content supersedes distributor communications.\"\n",
    "\n",
    "# Sample documents with conflicting information\n",
    "documents = [\n",
    "    \"Following detailed cost analysis and market research, we have implemented the following changes: AI training clusters will see a 15% uplift in raw compute performance, enterprise support packages are being restructured, and bulk procurement programs (100+ units) for the RTX 5090 Enterprise series will operate on a $2,899 baseline.\",\n",
    "    \"Enterprise pricing for the RTX 5090 GPU bulk orders (100+ units) is currently set at $3,100-$3,300 per unit. This pricing for RTX 5090 enterprise bulk orders has been confirmed across all major distribution channels.\",\n",
    "    \"RTX 5090 Enterprise GPU requires 450W TDP and 20% cooling overhead.\"\n",
    "]\n",
    "\n",
    "# Metadata that helps distinguish document sources and dates\n",
    "metadata = [\n",
    "    \"Date: January 15, 2025. Source: NVIDIA Enterprise Sales Portal. Classification: Internal Use Only\",\n",
    "    \"TechAnalytics Research Group. 11/30/2023.\",\n",
    "    \"January 25, 2025; NVIDIA Enterprise Sales Portal; Internal Use Only\"\n",
    "]\n",
    "\n",
    "# Use the instruction-following reranker model\n",
    "model = \"ctxl-rerank-en-v1-instruct\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xpLL8MEQA4Zt"
   },
   "source": [
    "Now let's see how the reranker processes our query and instructions to properly rank the documents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0mYFmjHqA4Zt",
    "outputId": "c111bdbc-fb4b-4e17-cfbc-fa15ce11c2b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reranking Results:\n",
      "==================================================\n",
      "{'results': [{'index': 0, 'relevance_score': 0.99995809}, {'index': 1, 'relevance_score': 0.99911428}, {'index': 2, 'relevance_score': 0.99082611}]}\n"
     ]
    }
   ],
   "source": [
    "# Execute the reranking\n",
    "rerank_response = client.rerank.create(\n",
    "    query=query,\n",
    "    instruction=instruction,\n",
    "    documents=documents,\n",
    "    metadata=metadata,\n",
    "    model=model\n",
    ")\n",
    "\n",
    "print(\"Reranking Results:\")\n",
    "print(\"=\" * 50)\n",
    "print(rerank_response.to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S0ZKTienA4Zu"
   },
   "source": [
    "Let's examine how the reranker prioritized the documents based on our instructions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bERNcAFwA4Zu",
    "outputId": "d4437b42-5b68-4ad7-dfa3-93aa0e48307e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ranked Documents (by relevance score):\n",
      "============================================================\n",
      "\n",
      "Rank 1: Score 1.0000\n",
      "Document 1:\n",
      "Content: Following detailed cost analysis and market research, we have implemented the following changes: AI ...\n",
      "Metadata: Date: January 15, 2025. Source: NVIDIA Enterprise Sales Portal. Classification: Internal Use Only\n",
      "----------------------------------------\n",
      "\n",
      "Rank 2: Score 0.9991\n",
      "Document 2:\n",
      "Content: Enterprise pricing for the RTX 5090 GPU bulk orders (100+ units) is currently set at $3,100-$3,300 p...\n",
      "Metadata: TechAnalytics Research Group. 11/30/2023.\n",
      "----------------------------------------\n",
      "\n",
      "Rank 3: Score 0.9908\n",
      "Document 3:\n",
      "Content: RTX 5090 Enterprise GPU requires 450W TDP and 20% cooling overhead....\n",
      "Metadata: January 25, 2025; NVIDIA Enterprise Sales Portal; Internal Use Only\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Display ranked results in a more readable format\n",
    "print(\"\\nRanked Documents (by relevance score):\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "for i, result in enumerate(rerank_response.results):\n",
    "    doc_index = result.index\n",
    "    score = result.relevance_score\n",
    "\n",
    "    print(f\"\\nRank {i+1}: Score {score:.4f}\")\n",
    "    print(f\"Document {doc_index + 1}:\")\n",
    "    print(f\"Content: {documents[doc_index][:100]}...\")\n",
    "    print(f\"Metadata: {metadata[doc_index]}\")\n",
    "    print(\"-\" * 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bV6C_ymGA4Zu"
   },
   "source": [
    "Let's compare how the same documents are ranked without specific instructions to see the difference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nl7zfIVpA4Zu",
    "outputId": "189a90c7-bd87-4a73-afa5-300db34b0aa4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ranking WITHOUT Instructions:\n",
      "==================================================\n",
      "Rank 1: Document 2, Score: 0.9998\n",
      "Rank 2: Document 1, Score: 0.9989\n",
      "Rank 3: Document 3, Score: 0.6510\n",
      "\n",
      "Ranking WITH Instructions:\n",
      "==================================================\n",
      "Rank 1: Document 1, Score: 1.0000\n",
      "Rank 2: Document 2, Score: 0.9991\n",
      "Rank 3: Document 3, Score: 0.9908\n"
     ]
    }
   ],
   "source": [
    "# Rerank without instructions for comparison\n",
    "rerank_no_instruction = client.rerank.create(\n",
    "    query=query,\n",
    "    documents=documents,\n",
    "    metadata=metadata,\n",
    "    model=model\n",
    ")\n",
    "\n",
    "print(\"\\nRanking WITHOUT Instructions:\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "for i, result in enumerate(rerank_no_instruction.results):\n",
    "    doc_index = result.index\n",
    "    score = result.relevance_score\n",
    "\n",
    "    print(f\"Rank {i+1}: Document {doc_index + 1}, Score: {score:.4f}\")\n",
    "\n",
    "print(\"\\nRanking WITH Instructions:\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "for i, result in enumerate(rerank_response.results):\n",
    "    doc_index = result.index\n",
    "    score = result.relevance_score\n",
    "\n",
    "    print(f\"Rank {i+1}: Document {doc_index + 1}, Score: {score:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "saQoDU986vn7"
   },
   "source": [
    "For more example code for Contextual AI's Reranker V2, see our [reranker examples notebook](https://github.com/ContextualAI/examples/tree/main/03-standalone-api/03-rerank?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0DQrfaZFA4Zu"
   },
   "source": [
    "## 3. Grounded Language Model (GLM)\n",
    "\n",
    "Contextual AI's Grounded Language Model (GLM) is the most grounded language model in the world, engineered specifically to minimize hallucinations for RAG and agentic use cases.\n",
    "\n",
    "With state-of-the-art performance on [FACTS](https://www.kaggle.com/benchmarks/google/facts-grounding) (the leading groundedness benchmark) and our customer datasets, the GLM is the single best language model for RAG and agentic use cases for which minimizing hallucinations is critical. You can trust that the GLM will stick to the knowledge sources you give it.\n",
    "\n",
    "In enterprise AI applications, hallucinations from the LLM pose a critical risk that can degrade customer experience, damage company reputation, and misguide business decisions. Yet the ability to hallucinate is seen as a useful feature in general-purpose foundation models, especially in serving consumer queries that require creative, novel responses. In contrast, the GLM is engineered specifically to minimize hallucinations for RAG and agentic use cases – delivering precise responses that are strongly grounded in and attributable to specific retrieved source data, not its parametric knowledge learned from training data.\n",
    "\n",
    "\n",
    "### Groundedness Definition\n",
    "\n",
    "\"Groundedness\" refers to the degree to which an LLM's generated output is supported by and accurately reflects the retrieved information provided to it. Given a query and a set of documents, a grounded model either responds only with relevant information from the documents or declines to answer if the documents are not relevant. In contrast, an ungrounded model may hallucinate based on patterns learned from its training data.\n",
    "\n",
    "For more information about GLM, you can read this [blog](https://contextual.ai/blog/introducing-grounded-language-model/?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_7o84lSkA4Zu"
   },
   "source": [
    "\n",
    "Let's demonstrate the GLM's ability to generate grounded responses using comprehensive knowledge sources about renewable energy in developing nations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "e-erJXyIA4Zu"
   },
   "outputs": [],
   "source": [
    "# Example conversation messages\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"What are the most promising renewable energy technologies for addressing climate change in developing nations?\"\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"assistant\",\n",
    "        \"content\": \"Based on current research, solar and wind power show significant potential for developing nations due to decreasing costs and scalability. Would you like to know more about specific implementation challenges and success stories?\"\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"Yes, please tell me about successful solar implementations in Africa and their economic impact, particularly focusing on rural electrification.\"\n",
    "    }\n",
    "]\n",
    "\n",
    "# Detailed knowledge sources with varied information\n",
    "knowledge = [\n",
    "    \"\"\"According to the International Renewable Energy Agency (IRENA) 2023 report:\n",
    "    - Solar PV installations in Africa reached 10.4 GW in 2022\n",
    "    - The cost of solar PV modules decreased by 80% between 2010 and 2022\n",
    "    - Rural electrification projects have provided power to 17 million households\"\"\",\n",
    "\n",
    "    \"\"\"Case Study: Rural Electrification in Kenya (2020-2023)\n",
    "    - 2.5 million households connected through mini-grid systems\n",
    "    - Average household income increased by 35% after electrification\n",
    "    - Local businesses reported 47% growth in revenue\n",
    "    - Education outcomes improved with 3 additional study hours per day\"\"\",\n",
    "\n",
    "    \"\"\"Economic Analysis of Solar Projects in Sub-Saharan Africa:\n",
    "    - Job creation: 25 jobs per MW of installed capacity\n",
    "    - ROI average of 12-15% for mini-grid projects\n",
    "    - Reduced energy costs by 60% compared to diesel generators\n",
    "    - Carbon emissions reduction: 2.3 million tonnes CO2 equivalent\"\"\",\n",
    "\n",
    "    \"\"\"Technical Specifications and Best Practices:\n",
    "    - Optimal solar panel efficiency in African climate conditions: 15-22%\n",
    "    - Battery storage requirements: 4-8 kWh per household\n",
    "    - Maintenance costs: $0.02-0.04 per kWh\n",
    "    - Expected system lifetime: 20-25 years\"\"\",\n",
    "\n",
    "    \"\"\"Social Impact Assessment:\n",
    "    - Women-led businesses increased by 45% in electrified areas\n",
    "    - Healthcare facilities reported 72% improvement in service delivery\n",
    "    - Mobile money usage increased by 60%\n",
    "    - Agricultural productivity improved by 28% with electric irrigation\"\"\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cR5vUNSMA4Zu"
   },
   "source": [
    "\n",
    "Now let's use the GLM to generate a grounded response based on the provided knowledge sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xFF31bZ3A4Zv",
    "outputId": "8fbe4abd-94e8-4b45-dc01-7511e31d96c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLM Grounded Response:\n",
      "==================================================\n",
      "Let me share the current state of solar energy implementation in Africa, focusing on rural electrification and its economic impact:\n",
      "\n",
      "Africa has seen significant solar PV adoption, with total installations reaching 10.4 GW by 2022, supported by an 80% decrease in solar PV module costs between 2010 and 2022.\n",
      "\n",
      "The impact on rural electrification has been particularly noteworthy:\n",
      "\n",
      "Key achievements include:\n",
      "- 17 million households have gained access to electricity through rural electrification projects\n",
      "- In Kenya specifically, 2.5 million households have been connected through mini-grid systems between 2020-2023\n",
      "\n",
      "The economic benefits have been substantial:\n",
      "\n",
      "Direct economic impacts include:\n",
      "- Average household income increased by 35% after electrification\n",
      "- Local businesses experienced 47% revenue growth\n",
      "- Solar projects create approximately 25 jobs per MW of installed capacity\n",
      "- Mini-grid projects typically achieve a 12-15% return on investment\n",
      "- Energy costs have been reduced by 60% compared to traditional diesel generators\n",
      "\n",
      "The broader social and economic transformation is also significant:\n",
      "\n",
      "Social and economic improvements include:\n",
      "- 45% increase in women-led businesses in electrified areas\n",
      "- 72% improvement in healthcare facility service delivery\n",
      "- 60% increase in mobile money usage\n",
      "- 28% improvement in agricultural productivity with electric irrigation\n",
      "\n",
      "From a technical perspective, the implementations have proven sustainable:\n",
      "\n",
      "Technical performance metrics show:\n",
      "- Systems maintain efficiency of 15-22% in African climate conditions\n",
      "- Expected system lifetimes of 20-25 years\n",
      "- Maintenance costs are relatively low at $0.02-0.04 per kWh\n",
      "\n",
      "These statistics demonstrate the transformative potential of solar energy in African rural development, offering both economic benefits and improved quality of life.\n"
     ]
    }
   ],
   "source": [
    "# Setup for direct API call\n",
    "base_url = \"https://api.contextual.ai/v1\"\n",
    "generate_api_endpoint = f\"{base_url}/generate\"\n",
    "\n",
    "headers = {\n",
    "    \"accept\": \"application/json\",\n",
    "    \"content-type\": \"application/json\",\n",
    "    \"authorization\": f\"Bearer {API_KEY}\"\n",
    "}\n",
    "\n",
    "# Configure the GLM request\n",
    "payload = {\n",
    "    \"model\": \"v1\",\n",
    "    \"messages\": messages,\n",
    "    \"knowledge\": knowledge,\n",
    "    \"avoid_commentary\": False,\n",
    "    \"max_new_tokens\": 1024,\n",
    "    \"temperature\": 0,\n",
    "    \"top_p\": 0.9\n",
    "}\n",
    "\n",
    "# Generate the response\n",
    "generate_response = requests.post(generate_api_endpoint, json=payload, headers=headers)\n",
    "\n",
    "print(\"GLM Grounded Response:\")\n",
    "print(\"=\" * 50)\n",
    "print(generate_response.json()['response'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3eqStijfA4Zv"
   },
   "source": [
    "The GLM has an `avoid_commentary` flag to control groundedness. Let's see how this affects the response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3pF1XfriA4Zv",
    "outputId": "b1b31f73-132f-4f7b-9adf-c269fc6e3b56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLM Response (with avoid_commentary=True):\n",
      "==================================================\n",
      "Africa has seen significant solar energy adoption, with total solar PV installations reaching 10.4 GW by 2022, driven by an 80% decrease in solar module costs since 2010. These efforts have already provided electricity to 17 million households through rural electrification projects.\n",
      "\n",
      "\n",
      "\n",
      "In Kenya, a notable case study from 2020-2023 demonstrated substantial economic benefits:\n",
      "- 2.5 million households were connected through mini-grid systems\n",
      "- Average household income increased by 35%\n",
      "- Local businesses saw 47% revenue growth\n",
      "- Students gained 3 additional study hours per day\n",
      "\n",
      "The economic impact extends beyond individual households:\n",
      "- Solar projects create 25 jobs per MW of installed capacity\n",
      "- Mini-grid projects typically achieve 12-15% ROI\n",
      "- Energy costs have been reduced by 60% compared to diesel generators\n",
      "- These initiatives have resulted in 2.3 million tonnes CO2 equivalent in emissions reduction\n",
      "\n",
      "The social impact has been particularly significant:\n",
      "- Women-led businesses increased by 45% in electrified areas\n",
      "- Healthcare facilities reported a 72% improvement in service delivery\n",
      "- Mobile money usage increased by 60%\n",
      "- Agricultural productivity improved by 28% with electric irrigation\n",
      "\n",
      "\n",
      "\n",
      "From a technical perspective, these systems have proven reliable with:\n",
      "- 20-25 year system lifetimes\n",
      "- Maintenance costs of $0.02-0.04 per kWh\n",
      "- Optimal solar panel efficiency of 15-22% in African climate conditions\n"
     ]
    }
   ],
   "source": [
    "# Generate response with avoid_commentary enabled\n",
    "payload_no_commentary = payload.copy()\n",
    "payload_no_commentary[\"avoid_commentary\"] = True\n",
    "\n",
    "generate_response_no_commentary = requests.post(generate_api_endpoint, json=payload_no_commentary, headers=headers)\n",
    "\n",
    "print(\"GLM Response (with avoid_commentary=True):\")\n",
    "print(\"=\" * 50)\n",
    "print(generate_response_no_commentary.json()['response'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gR-yLJNBA4Zv"
   },
   "source": [
    "\n",
    "Let's compare the two responses to understand the difference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8dbaPI7EA4Zv",
    "outputId": "faa742ab-f980-4323-8534-253c3b428286"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COMPARISON:\n",
      "============================================================\n",
      "\n",
      "1. Standard GLM Response (avoid_commentary=False):\n",
      "--------------------------------------------------\n",
      "Let me share the current state of solar energy implementation in Africa, focusing on rural electrification and its economic impact:\n",
      "\n",
      "Africa has seen significant solar PV adoption, with total installations reaching 10.4 GW by 2022, supported by an 80% decrease in solar PV module costs between 2010 and 2022.\n",
      "\n",
      "The impact on rural electrification has been particularly noteworthy:\n",
      "\n",
      "Key achievements include:\n",
      "- 17 million households have gained access to electricity through rural electrification projects\n",
      "- In Kenya specifically, 2.5 million households have been connected through mini-grid systems between 2020-2023\n",
      "\n",
      "The economic benefits have been substantial:\n",
      "\n",
      "Direct economic impacts include:\n",
      "- Average household income increased by 35% after electrification\n",
      "- Local businesses experienced 47% revenue growth\n",
      "- Solar projects create approximately 25 jobs per MW of installed capacity\n",
      "- Mini-grid projects typically achieve a 12-15% return on investment\n",
      "- Energy costs have been reduced by 60% compared to traditional diesel generators\n",
      "\n",
      "The broader social and economic transformation is also significant:\n",
      "\n",
      "Social and economic improvements include:\n",
      "- 45% increase in women-led businesses in electrified areas\n",
      "- 72% improvement in healthcare facility service delivery\n",
      "- 60% increase in mobile money usage\n",
      "- 28% improvement in agricultural productivity with electric irrigation\n",
      "\n",
      "From a technical perspective, the implementations have proven sustainable:\n",
      "\n",
      "Technical performance metrics show:\n",
      "- Systems maintain efficiency of 15-22% in African climate conditions\n",
      "- Expected system lifetimes of 20-25 years\n",
      "- Maintenance costs are relatively low at $0.02-0.04 per kWh\n",
      "\n",
      "These statistics demonstrate the transformative potential of solar energy in African rural development, offering both economic benefits and improved quality of life.\n",
      "\n",
      "\n",
      "2. Strict Grounding Mode (avoid_commentary=True):\n",
      "--------------------------------------------------\n",
      "Africa has seen significant solar energy adoption, with total solar PV installations reaching 10.4 GW by 2022, driven by an 80% decrease in solar module costs since 2010. These efforts have already provided electricity to 17 million households through rural electrification projects.\n",
      "\n",
      "\n",
      "\n",
      "In Kenya, a notable case study from 2020-2023 demonstrated substantial economic benefits:\n",
      "- 2.5 million households were connected through mini-grid systems\n",
      "- Average household income increased by 35%\n",
      "- Local businesses saw 47% revenue growth\n",
      "- Students gained 3 additional study hours per day\n",
      "\n",
      "The economic impact extends beyond individual households:\n",
      "- Solar projects create 25 jobs per MW of installed capacity\n",
      "- Mini-grid projects typically achieve 12-15% ROI\n",
      "- Energy costs have been reduced by 60% compared to diesel generators\n",
      "- These initiatives have resulted in 2.3 million tonnes CO2 equivalent in emissions reduction\n",
      "\n",
      "The social impact has been particularly significant:\n",
      "- Women-led businesses increased by 45% in electrified areas\n",
      "- Healthcare facilities reported a 72% improvement in service delivery\n",
      "- Mobile money usage increased by 60%\n",
      "- Agricultural productivity improved by 28% with electric irrigation\n",
      "\n",
      "\n",
      "\n",
      "From a technical perspective, these systems have proven reliable with:\n",
      "- 20-25 year system lifetimes\n",
      "- Maintenance costs of $0.02-0.04 per kWh\n",
      "- Optimal solar panel efficiency of 15-22% in African climate conditions\n",
      "\n",
      "\n",
      "Key Differences:\n",
      "- Standard mode may include helpful context and commentary\n",
      "- Strict mode focuses purely on information from knowledge sources\n",
      "- Both modes maintain strong grounding in provided sources\n"
     ]
    }
   ],
   "source": [
    "print(\"COMPARISON:\")\n",
    "print(\"=\" * 60)\n",
    "print(\"\\n1. Standard GLM Response (avoid_commentary=False):\")\n",
    "print(\"-\" * 50)\n",
    "print(generate_response.json()['response'])\n",
    "\n",
    "print(\"\\n\\n2. Strict Grounding Mode (avoid_commentary=True):\")\n",
    "print(\"-\" * 50)\n",
    "print(generate_response_no_commentary.json()['response'])\n",
    "\n",
    "print(\"\\n\\nKey Differences:\")\n",
    "print(\"- Standard mode may include helpful context and commentary\")\n",
    "print(\"- Strict mode focuses purely on information from knowledge sources\")\n",
    "print(\"- Both modes maintain strong grounding in provided sources\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0KM_Z2jaA4Zv"
   },
   "source": [
    "\n",
    "Let's test how the GLM handles a query when provided with irrelevant knowledge sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FWMQKt0-A4Zv",
    "outputId": "604800cf-ab96-4b88-94cf-35ee886aa829"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLM Response to Irrelevant Query:\n",
      "==================================================\n",
      "Query: What are the latest developments in quantum computing hardware?\n",
      "Knowledge provided: Renewable energy information\n",
      "\n",
      "GLM Response:\n",
      "I apologize, but I don't have any documentation about quantum computing hardware developments. The information I have access to is entirely focused on renewable energy and solar power implementations in Africa. If you'd like to know about solar energy developments or rural electrification projects, I'd be happy to help with that!\n"
     ]
    }
   ],
   "source": [
    "# Query about a completely different topic\n",
    "different_query = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"What are the latest developments in quantum computing hardware?\"\n",
    "    }\n",
    "]\n",
    "\n",
    "# Same renewable energy knowledge (irrelevant to quantum computing)\n",
    "irrelevant_payload = {\n",
    "    \"model\": \"v1\",\n",
    "    \"messages\": different_query,\n",
    "    \"knowledge\": knowledge,  # Still about renewable energy\n",
    "    \"avoid_commentary\": False,\n",
    "    \"max_new_tokens\": 512,\n",
    "    \"temperature\": 0,\n",
    "    \"top_p\": 0.9\n",
    "}\n",
    "\n",
    "irrelevant_response = requests.post(generate_api_endpoint, json=irrelevant_payload, headers=headers)\n",
    "\n",
    "print(\"GLM Response to Irrelevant Query:\")\n",
    "print(\"=\" * 50)\n",
    "print(\"Query: What are the latest developments in quantum computing hardware?\")\n",
    "print(\"Knowledge provided: Renewable energy information\")\n",
    "print(\"\\nGLM Response:\")\n",
    "print(irrelevant_response.json()['response'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zmOLoMIbEwfw"
   },
   "source": [
    "For more example code for Contextual AI's Grounded Language Model, see our [GLM examples notebook](https://github.com/ContextualAI/examples/tree/main/03-standalone-api/02-generate?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XebdZpTAA4Zv"
   },
   "source": [
    "## 4. LMUnit: Natural Language Unit Testing\n",
    "\n",
    " Evaluation, while not part of the core RAG pipeline, is a critical component to validating a RAG system before deploying to production. LMUnit is a language model optimized for evaluating natural language unit tests. LMUnit brings the rigor, familiarity, and accessibility of traditional software engineering unit testing to Large Language Model (LLM) evaluation.\n",
    "\n",
    "LMUnit sets the state of the art for fine-grained evaluation, as measured by FLASK and BiGGen Bench, and performs on par with frontier models for coarse evaluation of long-form responses (per LFQA). The model also demonstrates exceptional alignment with human preferences, ranking in the top 5 of the RewardBench benchmark with 93.5% accuracy.\n",
    "\n",
    "### Natural Language Unit Tests\n",
    "\n",
    "A unit test is a specific, clear, testable statement or question in natural language about a desirable quality of an LLM's response. Just as traditional unit tests check individual functions in software, unit tests in this paradigm evaluate discrete qualities of individual model outputs – from basic accuracy and formatting to complex reasoning and domain-specific requirements.\n",
    "\n",
    "### Types of Unit Tests\n",
    "\n",
    "- **Global unit tests**: Applied to all queries in an evaluation set (e.g., \"Does the response maintain a formal style?\")\n",
    "- **Targeted unit tests**: Focused assessment of query-level details (e.g., for \"Describe Stephen Curry's legacy\" → \"Does the response mention that Stephen Curry is the greatest shooter in NBA history?\")\n",
    "\n",
    "For more information about LMUnit, you can read this [blog](https://contextual.ai/lmunit/?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "60ULUZQ_A4Zv"
   },
   "source": [
    "Let's start with a basic example to understand how LMUnit works. LMUnit takes three inputs: a query, a response, and a unit test, then produces a continuous score between 1 and 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GdoByxEBA4Zv",
    "outputId": "b63b86c9-f6ae-4b1b-fdfb-6d6f806f43a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unit Test: Does the response avoid unnecessary information?\n",
      "Score: 1.96/5\n",
      "\n",
      "Analysis: The response includes additional quarterly trends beyond the specific Q4 request,\n",
      "which explains the lower score for avoiding unnecessary information.\n"
     ]
    }
   ],
   "source": [
    "# Simple example\n",
    "query = \"What was NVIDIA's Data Center revenue in Q4 FY25?\"\n",
    "\n",
    "response = \"\"\"NVIDIA's Data Center revenue for Q4 FY25 was $35,580 million.\n",
    "\n",
    "This represents a significant increase from the previous quarter (Q3 FY25) when Data Center revenue was $30,771 million.\n",
    "\n",
    "The full quarterly trend for Data Center revenue in FY25 was:\n",
    "- Q4 FY25: $35,580 million\n",
    "- Q3 FY25: $30,771 million\n",
    "- Q2 FY25: $26,272 million\n",
    "- Q1 FY25: $22,563 million\"\"\"\n",
    "\n",
    "unit_test = \"Does the response avoid unnecessary information?\"\n",
    "\n",
    "# Evaluate with LMUnit\n",
    "result = client.lmunit.create(\n",
    "    query=query,\n",
    "    response=response,\n",
    "    unit_test=unit_test\n",
    ")\n",
    "\n",
    "print(f\"Unit Test: {unit_test}\")\n",
    "print(f\"Score: {result.score}/5\")\n",
    "print(f\"\\nAnalysis: The response includes additional quarterly trends beyond the specific Q4 request,\")\n",
    "print(f\"which explains the lower score for avoiding unnecessary information.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0VXdKZCyA4Zv"
   },
   "source": [
    "Based on this score, you could adjust your system prompt to specifically exclude any information besides the exact response needed to address the query.\n",
    "\n",
    "Let's define a comprehensive set of unit tests for evaluating quantitative reasoning responses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IfkbdemeA4Zv",
    "outputId": "9aeb4270-32f4-4e0f-9e19-4c493e75c0c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unit Test Framework:\n",
      "==================================================\n",
      "1. ACCURACY: Does the response accurately extract specific numerical data from the documents?\n",
      "2. CAUSATION: Does the agent properly distinguish between correlation and causation?\n",
      "3. SYNTHESIS: Are multi-document comparisons performed correctly with accurate calculations?\n",
      "4. LIMITATIONS: Are potential limitations or uncertainties in the data clearly acknowledged?\n",
      "5. EVIDENCE: Are quantitative claims properly supported with specific evidence from the source documents?\n",
      "6. RELEVANCE: Does the response avoid unnecessary information?\n"
     ]
    }
   ],
   "source": [
    "# Define comprehensive unit tests for quantitative reasoning\n",
    "unit_tests = [\n",
    "    \"Does the response accurately extract specific numerical data from the documents?\",\n",
    "    \"Does the agent properly distinguish between correlation and causation?\",\n",
    "    \"Are multi-document comparisons performed correctly with accurate calculations?\",\n",
    "    \"Are potential limitations or uncertainties in the data clearly acknowledged?\",\n",
    "    \"Are quantitative claims properly supported with specific evidence from the source documents?\",\n",
    "    \"Does the response avoid unnecessary information?\"\n",
    "]\n",
    "\n",
    "# Create category mapping for visualization\n",
    "test_categories = {\n",
    "    'Does the response accurately extract specific numerical data': 'ACCURACY',\n",
    "    'Does the agent properly distinguish between correlation and causation': 'CAUSATION',\n",
    "    'Are multi-document comparisons performed correctly': 'SYNTHESIS',\n",
    "    'Are potential limitations or uncertainties in the data': 'LIMITATIONS',\n",
    "    'Are quantitative claims properly supported with specific evidence': 'EVIDENCE',\n",
    "    'Does the response avoid unnecessary information': 'RELEVANCE'\n",
    "}\n",
    "\n",
    "print(\"Unit Test Framework:\")\n",
    "print(\"=\" * 50)\n",
    "for i, test in enumerate(unit_tests, 1):\n",
    "    category = next((v for k, v in test_categories.items() if k.lower() in test.lower()), 'OTHER')\n",
    "    print(f\"{i}. {category}: {test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9zJrTwQA4Zv"
   },
   "source": [
    "We can also create sample prompt-response pairs for evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0ETQ153RA4Zv",
    "outputId": "5fd4f466-4b4f-45b6-e302-70f8a52c52c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Evaluation Dataset:\n",
      "                                                                                            prompt                                                                                                                                                                                                                                           response\n",
      "                                                 What was NVIDIA's Data Center revenue in Q4 FY25?                                                                                                                    NVIDIA's Data Center revenue for Q4 FY25 was $35,580 million. This represents a significant increase from the previous quarter.\n",
      "What is the correlation coefficient between Neptune's distance from the Sun and US burglary rates? According to the Tyler Vigen spurious correlations dataset, there is a correlation coefficient of 0.87 between Neptune's distance from the Sun and US burglary rates. However, this is clearly a spurious correlation with no causal relationship.\n",
      "                                    How did NVIDIA's total revenue change from Q1 FY22 to Q4 FY25?                                                                              NVIDIA's total revenue grew from $5.66 billion in Q1 FY22 to $60.9 billion in Q4 FY25, representing a massive increase driven primarily by AI and data center demand.\n"
     ]
    }
   ],
   "source": [
    "# Sample evaluation dataset\n",
    "evaluation_data = [\n",
    "    {\n",
    "        \"prompt\": \"What was NVIDIA's Data Center revenue in Q4 FY25?\",\n",
    "        \"response\": \"NVIDIA's Data Center revenue for Q4 FY25 was $35,580 million. This represents a significant increase from the previous quarter.\"\n",
    "    },\n",
    "    {\n",
    "        \"prompt\": \"What is the correlation coefficient between Neptune's distance from the Sun and US burglary rates?\",\n",
    "        \"response\": \"According to the Tyler Vigen spurious correlations dataset, there is a correlation coefficient of 0.87 between Neptune's distance from the Sun and US burglary rates. However, this is clearly a spurious correlation with no causal relationship.\"\n",
    "    },\n",
    "    {\n",
    "        \"prompt\": \"How did NVIDIA's total revenue change from Q1 FY22 to Q4 FY25?\",\n",
    "        \"response\": \"NVIDIA's total revenue grew from $5.66 billion in Q1 FY22 to $60.9 billion in Q4 FY25, representing a massive increase driven primarily by AI and data center demand.\"\n",
    "    }\n",
    "]\n",
    "\n",
    "eval_df = pd.DataFrame(evaluation_data)\n",
    "print(\"Sample Evaluation Dataset:\")\n",
    "print(eval_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t1VHFYoTA4Zv"
   },
   "source": [
    "\n",
    "Now let's run our unit tests across all evaluation examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "58BMFPg2A4Zv",
    "outputId": "e3ce5f35-79f1-4b8a-d2b5-315a39344c86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running comprehensive unit test evaluation...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing responses: 100%|███████████████████████| 3/3 [00:28<00:00,  9.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "EVALUATION 1\n",
      "============================================================\n",
      "Prompt: What was NVIDIA's Data Center revenue in Q4 FY25?\n",
      "Response: NVIDIA's Data Center revenue for Q4 FY25 was $35,580 million. This represents a significant increase...\n",
      "\n",
      "Unit Test Scores:\n",
      "  ACCURACY: 1.86/5\n",
      "  CAUSATION: 1.43/5\n",
      "  SYNTHESIS: 1.79/5\n",
      "  LIMITATIONS: 1.16/5\n",
      "  EVIDENCE: 1.41/5\n",
      "  RELEVANCE: 3.75/5\n",
      "\n",
      "============================================================\n",
      "EVALUATION 2\n",
      "============================================================\n",
      "Prompt: What is the correlation coefficient between Neptune's distance from the Sun and US burglary rates?\n",
      "Response: According to the Tyler Vigen spurious correlations dataset, there is a correlation coefficient of 0....\n",
      "\n",
      "Unit Test Scores:\n",
      "  ACCURACY: 4.20/5\n",
      "  CAUSATION: 4.90/5\n",
      "  SYNTHESIS: 2.75/5\n",
      "  LIMITATIONS: 4.66/5\n",
      "  EVIDENCE: 3.99/5\n",
      "  RELEVANCE: 3.75/5\n",
      "\n",
      "============================================================\n",
      "EVALUATION 3\n",
      "============================================================\n",
      "Prompt: How did NVIDIA's total revenue change from Q1 FY22 to Q4 FY25?\n",
      "Response: NVIDIA's total revenue grew from $5.66 billion in Q1 FY22 to $60.9 billion in Q4 FY25, representing ...\n",
      "\n",
      "Unit Test Scores:\n",
      "  ACCURACY: 3.24/5\n",
      "  CAUSATION: 2.76/5\n",
      "  SYNTHESIS: 3.18/5\n",
      "  LIMITATIONS: 1.15/5\n",
      "  EVIDENCE: 1.99/5\n",
      "  RELEVANCE: 3.60/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def run_unit_tests_with_progress(\n",
    "    df: pd.DataFrame,\n",
    "    unit_tests: List[str]\n",
    ") -> List[Dict]:\n",
    "    \"\"\"\n",
    "    Run unit tests with progress tracking and error handling.\n",
    "    \"\"\"\n",
    "    results = []\n",
    "\n",
    "    for idx in tqdm(range(len(df)), desc=\"Processing responses\"):\n",
    "        row = df.iloc[idx]\n",
    "        row_results = []\n",
    "\n",
    "        for test in unit_tests:\n",
    "            try:\n",
    "                result = client.lmunit.create(\n",
    "                    query=row['prompt'],\n",
    "                    response=row['response'],\n",
    "                    unit_test=test\n",
    "                )\n",
    "\n",
    "                row_results.append({\n",
    "                    'test': test,\n",
    "                    'score': result.score,\n",
    "                    'metadata': result.metadata if hasattr(result, 'metadata') else None\n",
    "                })\n",
    "\n",
    "            except Exception as e:\n",
    "                print(f\"Error with prompt {idx}, test '{test}': {e}\")\n",
    "                row_results.append({\n",
    "                    'test': test,\n",
    "                    'score': None,\n",
    "                    'error': str(e)\n",
    "                })\n",
    "\n",
    "        results.append({\n",
    "            'prompt': row['prompt'],\n",
    "            'response': row['response'],\n",
    "            'test_results': row_results\n",
    "        })\n",
    "\n",
    "    return results\n",
    "\n",
    "# Run the evaluation\n",
    "print(\"Running comprehensive unit test evaluation...\")\n",
    "results = run_unit_tests_with_progress(eval_df, unit_tests)\n",
    "\n",
    "# Display detailed results\n",
    "for i, result in enumerate(results):\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"EVALUATION {i+1}\")\n",
    "    print(f\"{'='*60}\")\n",
    "    print(f\"Prompt: {result['prompt']}\")\n",
    "    print(f\"Response: {result['response'][:100]}...\")\n",
    "    print(\"\\nUnit Test Scores:\")\n",
    "\n",
    "    for test_result in result['test_results']:\n",
    "        if 'score' in test_result and test_result['score'] is not None:\n",
    "            category = next((v for k, v in test_categories.items() if k.lower() in test_result['test'].lower()), 'OTHER')\n",
    "            print(f\"  {category}: {test_result['score']:.2f}/5\")\n",
    "        else:\n",
    "            print(f\"  Error: {test_result.get('error', 'Unknown error')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_UZkhzbBA4Zv"
   },
   "source": [
    "\n",
    "Let's create polar plots to visualize the unit test results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "id": "RtDfKi_iA4Zv",
    "outputId": "51be7ae3-ee7c-4e82-d89e-302d4fa1a7b6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def map_test_to_category(test_question: str) -> str:\n",
    "    \"\"\"Map the full test question to its category.\"\"\"\n",
    "    for key, value in test_categories.items():\n",
    "        if key.lower() in test_question.lower():\n",
    "            return value\n",
    "    return None\n",
    "\n",
    "def create_unit_test_plots(results: List[Dict], test_indices: Optional[List[int]] = None):\n",
    "    \"\"\"\n",
    "    Create polar plot(s) for unit test results.\n",
    "    \"\"\"\n",
    "    if test_indices is None:\n",
    "        test_indices = list(range(len(results)))\n",
    "    elif isinstance(test_indices, int):\n",
    "        test_indices = [test_indices]\n",
    "\n",
    "    categories = ['ACCURACY', 'CAUSATION', 'SYNTHESIS', 'LIMITATIONS', 'EVIDENCE', 'RELEVANCE']\n",
    "    angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False)\n",
    "    angles = np.concatenate((angles, [angles[0]]))\n",
    "\n",
    "    num_plots = len(test_indices)\n",
    "    fig = plt.figure(figsize=(6 * num_plots, 6))\n",
    "\n",
    "    for plot_idx, result_idx in enumerate(test_indices):\n",
    "        result = results[result_idx]\n",
    "        ax = plt.subplot(1, num_plots, plot_idx + 1, projection='polar')\n",
    "\n",
    "        scores = []\n",
    "        for category in categories:\n",
    "            score = None\n",
    "            for test_result in result['test_results']:\n",
    "                mapped_category = map_test_to_category(test_result['test'])\n",
    "                if mapped_category == category:\n",
    "                    score = test_result['score']\n",
    "                    break\n",
    "            scores.append(score if score is not None else 0)\n",
    "\n",
    "        scores = np.concatenate((scores, [scores[0]]))\n",
    "\n",
    "        ax.plot(angles, scores, 'o-', linewidth=2, color='blue')\n",
    "        ax.fill(angles, scores, alpha=0.25, color='blue')\n",
    "        ax.set_xticks(angles[:-1])\n",
    "        ax.set_xticklabels(categories)\n",
    "        ax.set_ylim(0, 5)\n",
    "        ax.grid(True)\n",
    "\n",
    "        for angle, score, category in zip(angles[:-1], scores[:-1], categories):\n",
    "            ax.text(angle, score + 0.2, f'{score:.1f}', ha='center', va='bottom')\n",
    "\n",
    "        prompt = result['prompt'][:50] + \"...\" if len(result['prompt']) > 50 else result['prompt']\n",
    "        ax.set_title(f\"Evaluation {result_idx + 1}\\n{prompt}\", pad=20)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    return fig\n",
    "\n",
    "# Create visualizations\n",
    "if len(results) > 0:\n",
    "    fig = create_unit_test_plots(results)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No results to visualize\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jrgsrwGmA4Zv"
   },
   "source": [
    "\n",
    "Let's analyze the overall performance across all categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M6BRhJCOA4Zv",
    "outputId": "bd0b6ade-475a-4aeb-acc9-79b0e42c5d9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Aggregate Performance by Category:\n",
      "==================================================\n",
      "             mean   std  count\n",
      "category                      \n",
      "ACCURACY     3.10  1.18      3\n",
      "CAUSATION    3.03  1.75      3\n",
      "EVIDENCE     2.46  1.35      3\n",
      "LIMITATIONS  2.32  2.02      3\n",
      "RELEVANCE    3.70  0.09      3\n",
      "SYNTHESIS    2.57  0.71      3\n",
      "\n",
      "Overall Statistics:\n",
      "Mean Score: 2.87/5\n",
      "Standard Deviation: 1.23\n",
      "Total Evaluations: 18\n"
     ]
    }
   ],
   "source": [
    "# Create aggregate analysis\n",
    "all_scores = []\n",
    "for result in results:\n",
    "    for test_result in result['test_results']:\n",
    "        if 'score' in test_result and test_result['score'] is not None:\n",
    "            category = map_test_to_category(test_result['test'])\n",
    "            all_scores.append({\n",
    "                'category': category,\n",
    "                'score': test_result['score'],\n",
    "                'test': test_result['test']\n",
    "            })\n",
    "\n",
    "scores_df = pd.DataFrame(all_scores)\n",
    "\n",
    "if not scores_df.empty:\n",
    "    # Calculate average scores by category\n",
    "    avg_scores = scores_df.groupby('category')['score'].agg(['mean', 'std', 'count']).round(2)\n",
    "\n",
    "    print(\"\\nAggregate Performance by Category:\")\n",
    "    print(\"=\" * 50)\n",
    "    print(avg_scores)\n",
    "\n",
    "    # Overall statistics\n",
    "    print(f\"\\nOverall Statistics:\")\n",
    "    print(f\"Mean Score: {scores_df['score'].mean():.2f}/5\")\n",
    "    print(f\"Standard Deviation: {scores_df['score'].std():.2f}\")\n",
    "    print(f\"Total Evaluations: {len(scores_df)}\")\n",
    "else:\n",
    "    print(\"No valid scores to analyze\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nkPk03yjGwuE"
   },
   "source": [
    "Interestingly, several of our unit tests are tricky to all score high on: if a response ranks high on CAUSATION (Does the agent properly distinguish between correlation and causation) and LIMITATIONS (Are potential limitations or uncertainties in the data clearly acknowledged?), it may be difficul to also score high on RELEVANCE (Does the response avoid unnecessary information?)\n",
    "\n",
    "You can try all of the analyses above with your own system by generating the responses, and testing those query-response pairs.\n",
    "\n",
    "For more example code for Contextual AI's LMUnit, see our [LMUnit examples notebook](https://github.com/ContextualAI/examples/tree/main/03-standalone-api/01-lmunit?utm_campaign=rag-techniques&utm_source=diamantai&utm_medium=github&utm_content=notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=all-rag-techniques--agentic-rag)\n"
   ]
  }
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